Open algorithms for identity federation

Abstract

The identity problem today is a data-sharing problem. Today the fixed attributes approach adopted by the consumer identity management industry provides only limited information about an individual, and therefore, is of limited value to the service providers and other participants in the identity ecosystem. This paper proposes the use of the Open Algorithms (OPAL) paradigm to address the increasing need for individuals and organizations to share data in a privacy-preserving manner. Instead of exchanging static or fixed attributes, participants in the ecosystem will be able to obtain better insight through a collective sharing of algorithms, governed through a trust network. Algorithms for specific datasets must be vetted to be privacy-preserving, fair and free from bias.